U.S. patent application number 17/152669 was filed with the patent office on 2021-11-18 for method and apparatus for base station audit correction in wireless communication networks.
The applicant listed for this patent is Samsung Electronics Co., Ltd.. Invention is credited to Samuel Albert, Hao Chen, Mandar N. Kulkarni, Vishnu Vardhan Ratnam, Rubayet Shafin, Yan Xin, Jianzhong Zhang.
Application Number | 20210360456 17/152669 |
Document ID | / |
Family ID | 1000005359675 |
Filed Date | 2021-11-18 |
United States Patent
Application |
20210360456 |
Kind Code |
A1 |
Ratnam; Vishnu Vardhan ; et
al. |
November 18, 2021 |
METHOD AND APPARATUS FOR BASE STATION AUDIT CORRECTION IN WIRELESS
COMMUNICATION NETWORKS
Abstract
A method for operating a base station is provided. The method
includes in response to a triggering event, fetching information on
a base station (BS) configuration parameters comprising a location,
a height, an antenna pattern, and topographical details surrounding
the BS; determining the BS configuration parameters that are error
prone and require re-estimation; obtain measurement reports created
by at least one user equipment (UE); determining an audit method to
perform an audit correction, the audit correction based on the one
or more of the BS configuration parameters to re-estimate,
available BS information and the measurement reports; performing
the audit correction, to obtain a result based on a computed score
for each candidate value of the BS configuration parameters;
generating, based on the result, one or more corrective actions;
and adjusting at least one of the BS configuration parameters based
on the one or more corrective actions.
Inventors: |
Ratnam; Vishnu Vardhan;
(Plano, TX) ; Albert; Samuel; (Robbinsville,
NJ) ; Shafin; Rubayet; (Allen, TX) ; Chen;
Hao; (Allen, TX) ; Xin; Yan; (Princeton,
NJ) ; Kulkarni; Mandar N.; (Richardson, TX) ;
Zhang; Jianzhong; (Plano, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Samsung Electronics Co., Ltd. |
Suwon-si |
|
KR |
|
|
Family ID: |
1000005359675 |
Appl. No.: |
17/152669 |
Filed: |
January 19, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
63025751 |
May 15, 2020 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W 16/18 20130101;
H04W 24/10 20130101; H04W 64/003 20130101; H04B 17/318 20150115;
H04W 48/16 20130101; H04B 17/101 20150115 |
International
Class: |
H04W 24/10 20060101
H04W024/10; H04B 17/10 20060101 H04B017/10; H04B 17/318 20060101
H04B017/318; H04W 16/18 20060101 H04W016/18; H04W 48/16 20060101
H04W048/16; H04W 64/00 20060101 H04W064/00 |
Claims
1. An apparatus comprising: a transceiver configured to communicate
via a wired or wireless communication medium; and a processor
configured to: in response to a triggering event, fetch information
on a base station (BS) configuration parameters, the BS
configuration parameters comprising at least one of: a location of
the BS, a height of the BS, an orientation of the BS, an antenna
pattern of the BS, and topographical details surrounding the BS;
determine one or more of the BS configuration parameters that are
error prone and require re-estimation; obtain measurement reports
created by at least one user equipment (UE), wherein the
measurement reports comprise a signal strength value and a location
of the at least one UE; determine an audit method to perform an
audit correction; perform the audit correction, to obtain a result
based on a computed score for each candidate value of the one or
more of the BS configuration parameters; generate, based on the
result, one or more corrective actions; and adjust at least one of
the BS configuration parameters based on the one or more corrective
actions.
2. The apparatus of claim 1, wherein the processor is configured to
determine the audit method based on an availability of a
cell-planning or signal strength prediction tool, and required
topographical information, wherein: when both the cell-planning or
signal strength prediction tool and required topographical
information is available, the computed score is based on a
comparison of user measurements to tool-predicted signal strength
values, and when one of the cell-planning or signal strength
prediction tool or topographical information is unavailable, the
computed score is based on a matching between the measurement
reports and some statistical rules, where the processor is
configured to use the determined audit method to calibrate
parameters of the signal strength prediction tool.
3. The apparatus of claim 1, wherein the audit correction is based
on the one or more of the BS configuration parameters to
re-estimate, available BS information and the measurement
reports.
4. The apparatus of claim 1, wherein the result comprises one of:
candidate values for the one or more of the BS configuration
parameters with a highest score; or a score of a particular choice
of the one or more of the BS configuration parameters, and wherein
the one or more corrective actions comprises at least one of:
updating of database parameters with the re-estimated parameters;
or raising an alarm for site visit based on a score value being
below a certain threshold.
5. The apparatus of claim 1, wherein, to obtain the measurement
reports, the processor is configured to receive the measurement
reports from the at least one UE based on at least one of:
periodically, in response to expiration of a timer; in response to
a specific user device condition; in response to a request from the
BS or apparatus, or in response to a signaling message that
includes a list of attributes to report or conditions for the at
least one UE to be eligible for reporting, or in response to the at
least one UE being in a specified location.
6. The apparatus of claim 1, wherein to estimate the one or more BS
configuration parameters, the processor is configured to one or
more of: filter one or more measurement reports that are faulty,
corrupted, or unwanted; estimate a reference signal received power
(RSRP) heat map from the BS to its surrounding for each candidate
value of the one or more of the BS configuration parameters based
on one or more of: wireless ray-tracing, statistical channel
models, and distance-dependent pathloss equations; and compare RSRP
values in measurement reports to the RSRP values in the RSRP heat
map to predict correct values of the one or more BS configuration
parameters; perform parameter estimation based on a weighting
applied to the at least one UE among a plurality of UEs providing
the measurement reports, where in the weighting is based on one or
more of a location error of the at least one UE or a reported RSRP
value; perform parameter estimation based on one or more of: an
estimate of a cell boundary of the BS, apriori knowledge of a
number of UE locations, or previous estimates of the one or more BS
configuration parameters; perform parameter estimation based using
a neural network configured to encode the measurement reports as an
image in which pixel coordinates represent location and pixel color
represents a reference signal received power (RSRP) value, wherein
a score function returned by the neural network comprises a measure
of an estimation accuracy.
7. The apparatus of claim 1, wherein the triggering event comprises
one of: expiration of a timer, an output of a root cause analysis
algorithm, comparison of the one or more of the BS configuration
parameters against stored values of the one or more of the BS
configuration parameters.
8. A method comprising: in response to a triggering event,
information on a base station (BS) configuration parameters, the BS
configuration parameters comprising at least one of: a location of
the BS, a height of the BS, an orientation of the BS, an antenna
pattern of the BS, and topographical details surrounding the BS;
determining one or more of the BS configuration parameters that are
error prone and require re-estimation; obtaining measurement
reports created by at least one user equipment (UE), wherein the
measurement reports comprise a signal strength value and a location
of the at least one UE; determining an audit method to perform an
audit correction; performing the audit correction to obtain a
result based on a computed score for each candidate value of the
one or more of the BS configuration parameters; generating, based
on the result, one or more corrective actions; and adjusting at
least one of the BS configuration parameters based on the one or
more corrective actions.
9. The method of claim 8, wherein the audit method is determined
based on an availability of a cell-planning or signal strength
prediction tool, and required topographical information, wherein:
when both the cell-planning or signal strength prediction tool and
required topographical information is available, the computed score
is based on a comparison of user measurements to tool-predicted
signal strength values, when one of the cell-planning or signal
strength prediction tool or topographical information is
unavailable, the computed score is based on a matching between the
measurement reports and some statistical rules, and where the
determined audit method is used to calibrate parameters of the
signal strength prediction tool.
10. The method of claim 8, wherein the audit correction is based on
the one or more of the BS configuration parameters to re-estimate,
available BS information and the measurement reports.
11. The method of claim 8, wherein the result comprises one of:
candidate values for the one or more of the BS configuration
parameters with a highest score; or a score of a particular choice
of the one or more of the BS configuration parameters, and wherein
the one or more corrective actions comprises at least one of:
updating of database parameters with the re-estimated parameters;
or raising an alarm for site visit based on a score value being
below a certain threshold.
12. The method of claim 8, wherein obtaining the measurement
reports comprises receiving the measurement reports from the at
least one UE based on at least one of: periodically, in response to
expiration of a timer; in response to a specific user device
condition; in response to a request from the BS or a core network
entity, or in response to a signaling message that includes a list
of attributes to report or conditions for the at least one UE to be
eligible for reporting, or in response to the at least one UE being
in a specified location.
13. The method of claim 8, wherein determining one or more of the
BS configuration parameters to estimate further comprises:
filtering one or more measurement reports that are faulty,
corrupted, or unwanted; estimating a reference signal received
power (RSRP) heat map from the BS to its surrounding for each
candidate value of the one or more of the BS configuration
parameters based on one or more of: wireless ray-tracing,
statistical channel models, and distance-dependent pathloss
equations; and comparing RSRP values in measurement reports to the
RSRP values in the RSRP heat map to predict correct values of the
one or more BS configuration parameters; performing parameter
estimation based on a weighting applied to the at least one UE
among a plurality of UEs providing the measurement reports, where
in the weighting is based on one or more of a location error of the
at least one UE or a reported RSRP value; performing parameter
estimation based on one or more of: an estimate of a cell boundary
of the BS, apriori knowledge of a number of UE locations, or
previous estimates of the one or more BS configuration parameters;
performing parameter estimation based using a neural network
configured to encode the measurement reports as an image in which
pixel coordinates represent location and pixel color represents a
reference signal received power (RSRP) value, wherein a score
function returned by the neural network comprises a measure of an
estimation accuracy.
14. The method of claim 8, wherein the triggering event comprises
one of: expiration of a timer, an output of a root cause analysis
algorithm, comparison of the one or more of the BS configuration
parameters against stored values of the one or more of the BS
configuration parameters.
15. A non-transitory computer readable medium comprising a
plurality of instructions that, when executed by a processor, is
configured to cause the processor to: in response to a triggering
event, fetch information on a base station (BS) configuration
parameters, the BS configuration parameters comprising at least one
of: a location of the BS, a height of the BS, an orientation of the
BS, an antenna pattern of the BS, and topographical details
surrounding the BS; determine one or more of the BS configuration
parameters that are error prone and require re-estimation; obtain
measurement reports created by at least one user equipment (UE),
wherein the measurement reports comprise a signal strength value
and a location of the at least one UE; determine an audit method to
perform an audit correction; perform the audit correction to obtain
a result based on a computed score for each candidate value of the
one or more of the BS configuration parameters; generate, based on
the result, one or more corrective actions; and adjust at least one
of the BS configuration parameters based on the one or more
corrective actions.
16. The non-transitory computer readable medium of claim 15,
wherein the plurality of instructions is configured to cause the
processor to determine the audit method based on an availability of
a cell-planning or signal strength prediction tool, and required
topographical information, wherein: when both the cell-planning or
signal strength prediction tool and required topographical
information is available, the computed score is based on a
comparison of user measurements to tool-predicted signal strength
values, and when one of the cell-planning or signal strength
prediction tool or topographical information is unavailable, the
computed score is based on a matching between the measurement
reports and some statistical rules, where the instructions are
configured to cause the processor to use the determined audit
method to calibrate parameters of the signal strength prediction
tool.
17. The non-transitory computer readable medium of claim 15,
wherein the audit correction is based on the one or more of the BS
configuration parameters to re-estimate, available BS information
and the measurement reports.
18. The non-transitory computer readable medium of claim 15,
wherein the result comprises one of: candidate values for the one
or more of the BS configuration parameters with a highest score; or
a score of a particular choice of the one or more of the BS
configuration parameters, and wherein the one or more corrective
actions comprises at least one of: updating of database parameters
with the re-estimated parameters; or raising an alarm for site
visit based on a score value being below a certain threshold.
19. The non-transitory computer readable medium of claim 15,
wherein, to obtain the measurement reports, the plurality of
instructions are configured to cause the processor to receive, from
the at least one UE, the measurement reports base on at least one
of: periodically, in response to expiration of a timer; in response
to a specific user device condition; in response to a request from
the BS or apparatus, or in response to a signaling message that
includes a list of attributes to report or conditions for the at
least one UE to be eligible for reporting, or in response to the at
least one UE being in a specified location.
20. The non-transitory computer readable medium of claim 15,
wherein to estimate the one or more BS configuration parameters,
the plurality of instructions is configured to cause the processor
to: filter one or more measurement reports that are faulty,
corrupted, or unwanted; estimate a reference signal received power
(RSRP) heat map from the BS to its surrounding for each candidate
value of the one or more of the BS configuration parameters based
on one or more of: wireless ray-tracing, statistical channel
models, and distance-dependent pathloss equations; compare RSRP
values in measurement reports to the RSRP values in the RSRP heat
map to predict correct values of the one or more BS configuration
parameters; perform parameter estimation when the measurement
reports include a location error of the at least one UE; perform
parameter estimation based on a weighting applied to the at least
one UE among a plurality of UEs providing the measurement reports;
perform parameter estimation based on one or more of: an estimate
of a cell boundary of the BS, apriori knowledge of a number of UE
locations, or previous estimates of the one or more BS
configuration parameters; perform parameter estimation based using
a neural network configured to encode the measurement reports as an
image in which pixel coordinates represent location and pixel color
represents a reference signal received power (RSRP) value, wherein
a score function returned by the neural network comprises a measure
of an estimation accuracy.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS AND CLAIM OF PRIORITY
[0001] The present application claims priority to U.S. Provisional
Patent Application No. 63/025,751 filed on May 15, 2020. The
content of the above-identified patent document is incorporated
herein by reference.
TECHNICAL FIELD
[0002] The present disclosure relates to electronic devices and
methods for predicting and updating base station configuration
parameters in wireless communication networks.
BACKGROUND
[0003] Configuration parameters of a Base Station (BS), including
location, height, azimuth and tilt angles, and pattern of antenna
array, significantly impact the coverage of the BS. Consequently,
these parameters are set judiciously so that the overall coverage
and throughput of the cellular network are maximized. Although the
BS location, antenna pattern, and height are fixed in most cases,
azimuth and tilt angles may often need to be manually reconfigured
based on a network condition. Such reconfiguration tasks are
susceptible to human errors and use of uncalibrated devices,
leading to a mismatch between the actual configuration and the
prescribed configuration stored in the centralized database. These
errors may get further exacerbated by misalignment due to natural
phenomena, such as heavy wind, earthquakes, and the like. Such
errors can adversely impact network automation optimization
applications, resulting in detrimental effects such as coverage
holes, cell overshooting etc. Hence, operators need to invest
significant effort to routinely re-estimate the BS parameters,
especially the azimuth and tilt angles, to minimize these
errors.
SUMMARY
[0004] Embodiments of the present disclosure provide methods and
apparatuses for automatic and accurate estimation of the
error-prone BS configuration parameters with minimal
human-intervention in an advanced wireless communication
system.
[0005] In one embodiment, an apparatus is provided. The apparatus
includes a transceiver configured to communicate via a wired or
wireless communication medium; and a processor. The processor is
configured to: in response to a triggering event, fetch information
on a base station (BS) configuration parameters, the BS
configuration parameters comprising at least one of: a location of
the BS, a height of the BS, an antenna pattern of the BS, and
topographical details surrounding the BS; determine one or more of
the BS configuration parameters that are error prone and require
re-estimation; obtain measurement reports created by at least one
user equipment (UE), wherein the measurement reports comprise a
signal strength value and a location of the at least one UE;
determine an audit method to perform an audit correction; perform
the audit correction, to obtain a result based on a computed score
for each candidate value of the one or more of the BS configuration
parameters; generate, based on the result, one or more corrective
actions; and adjust at least one of the BS configuration parameters
based on the one or more corrective actions.
[0006] In another embodiment, a method is provided. The method
includes in response to a triggering event, information on a base
station (BS) configuration parameters, the BS configuration
parameters comprising at least one of: a location of the BS, a
height of the BS, an antenna pattern of the BS, and topographical
details surrounding the BS. The method also includes determining
one or more of the BS configuration parameters that are error prone
and require re-estimation. The method also includes obtaining
measurement reports created by at least one user equipment (UE),
wherein the measurement reports comprise a signal strength value
and a location of the at least one UE. The method also includes
determining an audit method to perform an audit correction. The
method also includes performing the audit correction to obtain a
result based on a computed score for each candidate value of the
one or more of the BS configuration parameters. The method further
includes generating, based on the result, one or more corrective
actions; and adjusting at least one of the BS configuration
parameters based on the one or more corrective actions.
[0007] In yet another embodiment, a non-transitory computer
readable medium is provided. The non-transitory computer readable
medium a plurality of instructions that, when executed by a
processor, is configured to cause the processor to: in response to
a triggering event, fetch information on a base station (B S)
configuration parameters, the BS configuration parameters
comprising at least one of: a location of the BS, a height of the
BS, an antenna pattern of the BS, and topographical details
surrounding the BS; determine one or more of the BS configuration
parameters that are error prone and require re-estimation; obtain
measurement reports created by at least one user equipment (UE),
wherein the measurement reports comprise a signal strength value
and a location of the at least one UE; determine an audit method to
perform an audit correction; perform the audit correction to obtain
a result based on a computed score for each candidate value of the
one or more of the BS configuration parameters; generate, based on
the result, one or more corrective actions; and adjust at least one
of the BS configuration parameters based on the one or more
corrective actions.
[0008] Other technical features may be readily apparent to one
skilled in the art from the following figures, descriptions, and
claims.
[0009] Before undertaking the DETAILED DESCRIPTION below, it may be
advantageous to set forth definitions of certain words and phrases
used throughout this patent document. The term "couple" and its
derivatives refer to any direct or indirect communication between
two or more elements, whether or not those elements are in physical
contact with one another. The terms "transmit," "receive," and
"communicate," as well as derivatives thereof, encompass both
direct and indirect communication. The terms "include" and
"comprise," as well as derivatives thereof, mean inclusion without
limitation. The term "or" is inclusive, meaning and/or. The phrase
"associated with," as well as derivatives thereof, means to
include, be included within, interconnect with, contain, be
contained within, connect to or with, couple to or with, be
communicable with, cooperate with, interleave, juxtapose, be
proximate to, be bound to or with, have, have a property of, have a
relationship to or with, or the like. The term "controller" means
any device, system or part thereof that controls at least one
operation. Such a controller may be implemented in hardware or a
combination of hardware and software and/or firmware. The
functionality associated with any particular controller may be
centralized or distributed, whether locally or remotely. The phrase
"at least one of," when used with a list of items, means that
different combinations of one or more of the listed items may be
used, and only one item in the list may be needed. For example, "at
least one of: A, B, and C" includes any of the following
combinations: A, B, C, A and B, A and C, B and C, and A and B and
C.
[0010] Moreover, various functions described below can be
implemented or supported by one or more computer programs, each of
which is formed from computer readable program code and embodied in
a computer readable medium. The terms "application" and "program"
refer to one or more computer programs, software components, sets
of instructions, procedures, functions, objects, classes,
instances, related data, or a portion thereof adapted for
implementation in a suitable computer readable program code. The
phrase "computer readable program code" includes any type of
computer code, including source code, object code, and executable
code. The phrase "computer readable medium" includes any type of
medium capable of being accessed by a computer, such as read only
memory (ROM), random access memory (RAM), a hard disk drive, a
compact disc (CD), a digital video disc (DVD), or any other type of
memory. A "non-transitory" computer readable medium excludes wired,
wireless, optical, or other communication links that transport
transitory electrical or other signals. A non-transitory computer
readable medium includes media where data can be permanently stored
and media where data can be stored and later overwritten, such as a
rewritable optical disc or an erasable memory device.
[0011] Definitions for other certain words and phrases are provided
throughout this patent document. Those of ordinary skill in the art
should understand that in many if not most instances, such
definitions apply to prior as well as future uses of such defined
words and phrases.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] For a more complete understanding of the present disclosure
and its advantages, reference is now made to the following
description taken in conjunction with the accompanying drawings, in
which like reference numerals represent like parts:
[0013] FIG. 1 illustrates an example wireless network according to
embodiments of the present disclosure;
[0014] FIG. 2 illustrates an example gNB according to embodiments
of the present disclosure;
[0015] FIG. 3 illustrates an example UE according to embodiments of
the present disclosure;
[0016] FIG. 4A illustrates a high-level diagram of an orthogonal
frequency division multiple access transmit path according to
embodiments of the present disclosure;
[0017] FIG. 4B illustrates a high-level diagram of an orthogonal
frequency division multiple access receive path according to
embodiments of the present disclosure;
[0018] FIGS. 5A-D illustrate base station configuration parameters
and corresponding effect on a received power heat map according to
embodiments of the present disclosure;
[0019] FIG. 6 illustrates a site audit process according to
embodiments of the present disclosure;
[0020] FIG. 7 illustrates a process for generation and collection
of measurement reports by a user equipment according to embodiments
of the present disclosure;
[0021] FIG. 8 illustrates a process for generation and collection
of measurement reports by a base station according to embodiments
of the present disclosure;
[0022] FIGS. 9 and 10 illustrate a model-based base station
parameter estimation according to embodiments of the present
disclosure;
[0023] FIGS. 11 and 12 illustrate a model-free base station
parameter estimation according to embodiments of the present
disclosure;
[0024] FIG. 13 illustrates location errors in user reports
according to embodiments of the present disclosure;
[0025] FIG. 14 illustrates a cell association boundary based
parameter estimation according to embodiments of the present
disclosure;
[0026] FIG. 15 illustrates deep learning based parameter estimation
according to embodiments of the present disclosure; and
[0027] FIG. 16 illustrates a core network entity operation in site
audit correction according to embodiments of the present
disclosure.
DETAILED DESCRIPTION
[0028] FIG. 1 through FIG. 15, discussed below, and the various
embodiments used to describe the principles of the present
disclosure in this patent document are by way of illustration only
and should not be construed in any way to limit the scope of the
disclosure. Those skilled in the art will understand that the
principles of the present disclosure may be implemented in any
suitably arranged system or device.
[0029] Aspects, features, and advantages of the disclosure are
readily apparent from the following detailed description, simply by
illustrating a number of particular embodiments and
implementations, including the best mode contemplated for carrying
out the disclosure. The disclosure is also capable of other and
different embodiments, and its several details can be modified in
various obvious respects, all without departing from the spirit and
scope of the disclosure. Accordingly, the drawings and description
are to be regarded as illustrative in nature, and not as
restrictive. The disclosure is illustrated by way of example, and
not by way of limitation, in the figures of the accompanying
drawings.
[0030] Current mechanisms for updating or correcting the base
station (BS) parameters involve identifying poorly performing cells
using tertiary metrics, and deploying a site engineer to go
diagnose the reason for poor performance. Since the causes for poor
performance can by many, this approach can lead to a large number
of false positives. Additionally, deploying a site engineer to
diagnose errors is a time consuming and expensive endeavor and is
not a scalable solution for a nation-wide network with hundreds of
thousands of BSs.
[0031] To address this issue, embodiments of the present disclosure
provides mechanisms for automatic and accurate estimation of the
error-prone BS configuration parameters with minimal
human-intervention. Embodiments of the present disclosure provide
methods to predict the configuration parameters of base-stations
without manual intervention or inspection. In this approach, some
users (subscribers and/or agents deployed by service provider) in
the cell periodically report their measured reference signal
receive power (RSRP) values and locations to the BS. A core network
entity uses these reports to predict the base-station parameters.
Certain embodiments provide new methodologies for estimating and
correcting multiple BS parameters, including both azimuth and
M-tilt angles. Certain embodiments provide estimation methodologies
that can work both with or without the availability of an RSRP
prediction tool like ray-tracing, account for any modeling errors
and inaccuracies in user reports.
[0032] Knowledge of currently configured base-station parameters is
of vital importance for network automation optimization.
Embodiments of the present disclosure significantly reduce the cost
and time required for accurately estimating these base-station
parameters. Accurate knowledge of these parameters can further
boost the overall network performance significantly.
[0033] In the following, for brevity, both Frequency Division
Duplexing (FDD) and Time Division Duplexing (TDD) are considered as
the duplex method for both DL and UL signaling.
[0034] Although exemplary descriptions and embodiments to follow
assume orthogonal frequency division multiplexing (OFDM) or
orthogonal frequency division multiple access (OFDMA), this
disclosure can be extended to other OFDM-based transmission
waveforms or multiple access schemes such as filtered OFDM
(F-OFDM).
[0035] The present disclosure covers several components which can
be used in conjunction or in combination with one another, or can
operate as standalone schemes.
[0036] To meet the demand for wireless data traffic having
increased since deployment of 4G communication systems, efforts
have been made to develop an improved 5G or pre-5G communication
system, as well as non-terrestrial networks (NTN). Therefore, the
5G or pre-5G communication system is also called a "beyond 4G
network" or a "post LTE system."
[0037] The 5G communication system is considered to be implemented
in higher frequency (mmWave) bands, e.g., 60 GHz bands, so as to
accomplish higher data rates. To decrease propagation loss of the
radio waves and increase the transmission coverage, the
beamforming, massive multiple-input multiple-output (MIMO), full
dimensional MIMO (FD-MIMO), array antenna, an analog beam forming,
large scale antenna techniques and the like are discussed in 5G
communication systems.
[0038] In addition, in 5G communication systems, development for
system network improvement is under way based on advanced small
cells, cloud radio access networks (RANs), ultra-dense networks,
device-to-device (D2D) communication, wireless backhaul
communication, moving network, cooperative communication,
coordinated multi-points (CoMP) transmission and reception,
interference mitigation and cancellation and the like.
[0039] In the 5G system, hybrid frequency shift keying (FSK) and
quadrature amplitude modulation (FQAM) and sliding window
superposition coding (SWSC) as an adaptive modulation and coding
(AMC) technique, and filter bank multi carrier (FBMC),
non-orthogonal multiple access (NOMA), and sparse code multiple
access (SCMA) as an advanced access technology have been
developed.
[0040] FIGS. 1-4B below describe various embodiments implemented in
wireless communications systems and with the use of orthogonal
frequency division multiplexing (OFDM) or orthogonal frequency
division multiple access (OFDMA) communication techniques. The
descriptions of FIGS. 1-3 are not meant to imply physical or
architectural limitations to the manner in which different
embodiments may be implemented. Different embodiments of the
present disclosure may be implemented in any suitably-arranged
communications system.
[0041] Certain embodiments of the disclosure may be derived by
utilizing a combination of several of the embodiments listed below.
Also, it should be noted that further embodiments may be derived by
utilizing a particular subset of operational steps as disclosed in
each of these embodiments. This DOI should be understood to cover
all such embodiments.
[0042] Certain embodiments of the present disclosure are described
assuming cellular DL communications. However, the same/similar
principles and related signaling methods & configurations can
also be used for cellular UL & sidelink (SL).
[0043] FIG. 1 illustrates an example wireless network according to
embodiments of the present disclosure. The embodiment of the
wireless network shown in FIG. 1 is for illustration only. Other
embodiments of the wireless network 100 could be used without
departing from the scope of this disclosure.
[0044] As shown in FIG. 1, the wireless network includes a gNB 101,
a gNB 102, and a gNB 103. The gNB 101 communicates with the gNB 102
and the gNB 103. The gNB 101 also communicates with at least one
core network 130, such as the Internet, a proprietary Internet
Protocol (IP) network, or other data network.
[0045] The gNB 102 provides wireless broadband access to the
network 130 for a first plurality of user equipments (UEs) within a
coverage area 120 of the gNB 102. The first plurality of UEs
includes a UE 111, which may be located in a small business; a UE
112, which may be located in an enterprise (E); a UE 113, which may
be located in a WiFi hotspot (HS); a UE 114, which may be located
in a first residence (R); a UE 115, which may be located in a
second residence (R); and a UE 116, which may be a mobile device
(M), such as a cell phone, a wireless laptop, a wireless PDA, or
the like. The gNB 103 provides wireless broadband access to the
network 130 for a second plurality of UEs within a coverage area
125 of the gNB 103. The second plurality of UEs includes the UE 115
and the UE 116 as well as a UE 117, which may be located in a third
residence (R), and a UE 118, which may be located in another
residence (R). In some embodiments, one or more of the gNBs 101-103
may communicate with each other and with the UEs 111-118 using 5G,
LTE, LTE-A, WiMAX, WiFi, or other wireless communication
techniques.
[0046] Depending on the network type, the term "base station" or
"BS" can refer to any component (or collection of components)
configured to provide wireless access to a network, such as
transmit point (TP), transmit-receive point (TRP), an enhanced base
station (eNodeB or eNB), a 5G base station (gNB), a macrocell, a
femtocell, a WiFi access point (AP), or other wirelessly enabled
devices. Base stations may provide wireless access in accordance
with one or more wireless communication protocols, e.g., 5G 3GPP
new radio interface/access (NR), long term evolution (LTE), LTE
advanced (LTE-A), high speed packet access (HSPA), Wi-Fi
802.11a/b/g/n/ac, etc. For the sake of convenience, the terms "BS"
and "TRP" are used interchangeably in this patent document to refer
to network infrastructure components that provide wireless access
to remote terminals. Also, depending on the network type, the term
"user equipment" or "UE" can refer to any component such as "mobile
station," "subscriber station," "remote terminal," "wireless
terminal," "receive point," or "user device." For the sake of
convenience, the terms "user equipment" and "UE" are used in this
patent document to refer to remote wireless equipment that
wirelessly accesses a BS, whether the UE is a mobile device (such
as a mobile telephone or smartphone) or is normally considered a
stationary device (such as a desktop computer or vending
machine).
[0047] Dotted lines show the approximate extents of the coverage
areas 120 and 125, which are shown as approximately circular for
the purposes of illustration and explanation only. It should be
clearly understood that the coverage areas associated with gNBs,
such as the coverage areas 120 and 125, may have other shapes,
including irregular shapes, depending upon the configuration of the
gNBs and variations in the radio environment associated with
natural and man-made obstructions.
[0048] As described in more detail below, one or more of gNB 101,
gNB 102 and gNB 103 include a two-dimensional (2D) antenna arrays
as described in embodiments of the present disclosure. In some
embodiments, one or more of gNB 101, gNB 102 and gNB 103 support
the codebook design and structure for systems having 2D antenna
arrays.
[0049] As described in more detail below, one or more of the gNBs
101-103 include circuitry, programing, or a combination thereof,
for performing the audit correction to obtain a result based on a
computed score for each candidate value of the one or more of the
BS configuration parameters; generating, based on the result, one
or more corrective actions; and adjusting at least one of the BS
configuration parameters based on the one or more corrective
actions.
[0050] In certain embodiments, gNB 102 may be connected to the core
network 130 by a fiber/wired backhaul link. As indicated herein
above, gNB 102 serves multiple UEs 111-116 via wireless interfaces
respectively. Using this wireless interface, a UE 116 receives and
transmit signals to gNB 102. Using signals received from a
non-serving gNB 103, a UE 116 may also receive signals from a
neighboring gNB 103. The core network 130 may further include a
core network entity (CNE) 135, which responsible for the task of
site audit correction, as described herein below. In certain
embodiments, the CNE 135 is a base station, such as gNB 103.
[0051] Although FIG. 1 illustrates one example of a wireless
network, various changes may be made to FIG. 1. For example, the
wireless network could include any number of gNBs and any number of
UEs in any suitable arrangement. Also, the gNB 101 could
communicate directly with any number of UEs and provide those UEs
with wireless broadband access to the network 130. Similarly, each
gNB 102-103 could communicate directly with the network 130 and
provide UEs with direct wireless broadband access to the network
130. Further, the gNBs 101, 102, and/or 103 could provide access to
other or additional external networks, such as external telephone
networks or other types of data networks.
[0052] FIG. 2 illustrates an example gNB 102 according to
embodiments of the present disclosure. The embodiment of the gNB
102 illustrated in FIG. 2 is for illustration only, and the gNBs
101 and 103 of FIG. 1 could have the same or similar configuration.
However, gNBs come in a wide variety of configurations, and FIG. 2
does not limit the scope of this disclosure to any particular
implementation of a gNB.
[0053] As shown in FIG. 2, the gNB 102 includes multiple antennas
205a-205n, multiple RF transceivers 210a-210n, transmit (TX)
processing circuitry 215, and receive (RX) processing circuitry
220. The gNB 102 also includes a controller/processor 225, a memory
230, and a backhaul or network interface 235.
[0054] The RF transceivers 210a-210n receive, from the antennas
205a-205n, incoming RF signals, such as signals transmitted by UEs
in the network 100. The RF transceivers 210a-210n down-convert the
incoming RF signals to generate IF or baseband signals. The IF or
baseband signals are sent to the RX processing circuitry 220, which
generates processed baseband signals by filtering, decoding, and/or
digitizing the baseband or IF signals. The RX processing circuitry
220 transmits the processed baseband signals to the
controller/processor 225 for further processing. The TX processing
circuitry 215 receives analog or digital data (such as voice data,
web data, e-mail, or interactive video game data) from the
controller/processor 225. The TX processing circuitry 215 encodes,
multiplexes, and/or digitizes the outgoing baseband data to
generate processed baseband or IF signals. The RF transceivers
210a-210n receive the outgoing processed baseband or IF signals
from the TX processing circuitry 215 and up-converts the baseband
or IF signals to RF signals that are transmitted via the antennas
205a-205n. In certain embodiments, the RF transceivers 210a-210n
perform transmission and reception via radio waves or wired
communications. For example, communications may be accomplished via
wired connections, optical fiber systems, communication satellites,
radio waves, and the like.
[0055] The controller/processor 225 can include one or more
processors or other processing devices that control the overall
operation of the gNB 102. For example, the controller/processor 225
could control the reception of forward channel signals and the
transmission of reverse channel signals by the RF transceivers
210a-210n, the RX processing circuitry 220, and the TX processing
circuitry 215 in accordance with well-known principles. The
controller/processor 225 could support additional functions as
well, such as more advanced wireless communication functions. That
is, the controller/processor 225 can perform a blind interference
sensing (BIS) process, such as performed by a BIS algorithm, and
decode the received signal subtracted by the interfering signals.
Any of a wide variety of other functions can be supported in the
gNB 102 by the controller/processor 225. In some embodiments, the
controller/processor 225 includes at least one microprocessor or
microcontroller
[0056] In certain embodiments, the controller/processor 225 could
support beam forming or directional routing operations in which
outgoing signals from multiple antennas 205a-205n are weighted
differently to effectively steer the outgoing signals in a desired
direction. Any of a wide variety of other functions could be
supported in the gNB 102 by the controller/processor 225.
[0057] The controller/processor 225 is also capable of executing
programs and other processes resident in the memory 230, such as an
OS. The controller/processor 225 can move data into or out of the
memory 230 as required by an executing process.
[0058] The controller/processor 225 is also capable of supporting
channel quality measurement and reporting for systems having 2D
antenna arrays as described in embodiments of the present
disclosure. In some embodiments, the controller/processor 225
supports communications between entities, such as web RTC. The
controller/processor 225 can move data into or out of the memory
230 as required by an executing process.
[0059] The controller/processor 225 is also coupled to the backhaul
or network interface 235. The backhaul or network interface 235
allows the gNB 102 to communicate with other devices or systems
over a backhaul connection or over a network. The interface 235
could support communications over any suitable wired or wireless
connection(s). For example, when the gNB 102 is implemented as part
of a cellular communication system (such as one supporting 5G, LTE,
or LTE-A), the interface 235 could allow the gNB 102 to communicate
with other gNBs over a wired or wireless backhaul connection. When
the gNB 102 is implemented as an access point, the interface 235
could allow the gNB 102 to communicate over a wired or wireless
local area network or over a wired or wireless connection to a
larger network (such as the Internet). The interface 235 includes
any suitable structure supporting communications over a wired or
wireless connection, such as an Ethernet or RF transceiver.
[0060] The memory 230 is coupled to the controller/processor 225.
Part of the memory 230 could include a RAM, and another part of the
memory 230 could include a Flash memory or other ROM. In certain
embodiments, a plurality of instructions, such as a BIS algorithm
is stored in memory 230. The plurality of instructions is
configured to cause the controller/processor 225 to perform the BIS
process and to decode a received signal after subtracting out at
least one interfering signal determined by the BIS algorithm.
[0061] As described in more detail below, the transmit and receive
paths of the gNB 102 (implemented using the RF transceivers
210a-210n, TX processing circuitry 215, and/or RX processing
circuitry 220) support configuring one or more repetitions for one
or more of a physical downlink control channel (PDCCH), a physical
downlink shared channel (PDSCH), or a physical uplink shared
channel (PUSCH), wherein a configuration information comprises a
parameter to extend a maximum number of repetitions for the channel
and transmitting the one or more repetitions according to the
configuration information.
[0062] Although FIG. 2 illustrates one example of gNB 102, various
changes may be made to FIG. 2. For example, the gNB 102 could
include any number of each component shown in FIG. 2. As a
particular example, an access point could include a number of
interfaces 235, and the controller/processor 225 could support
routing functions to route data between different network
addresses. As another particular example, while shown as including
a single instance of TX processing circuitry 215 and a single
instance of RX processing circuitry 220, the gNB 102 could include
multiple instances of each (such as one per RF transceiver). Also,
various components in FIG. 2 could be combined, further subdivided,
or omitted and additional components could be added according to
particular needs.
[0063] FIG. 3 illustrates an example UE 116 according to
embodiments of the present disclosure. The embodiment of the UE 116
illustrated in FIG. 3 is for illustration only, and the UEs 111-115
of FIG. 1 could have the same or similar configuration. However,
UEs come in a wide variety of configurations, and FIG. 3 does not
limit the scope of this disclosure to any particular implementation
of a UE.
[0064] As shown in FIG. 3, the UE 116 includes an antenna 305, a
radio frequency (RF) transceiver 310, TX processing circuitry 315,
a microphone 320, and receive (RX) processing circuitry 325. The UE
116 also includes a speaker 330, a processor 340, an input/output
(I/O) interface (IF) 345, a touchscreen 350 (or key pad), a display
355, and a memory 360. The memory 360 includes an operating system
(OS) 361 and one or more applications 362.
[0065] The RF transceiver 310 receives, from the antenna 305, an
incoming RF signal transmitted by a gNB of the network 100. The RF
transceiver 310 down-converts the incoming RF signal to generate an
intermediate frequency (IF) or baseband signal. The IF or baseband
signal is sent to the RX processing circuitry 325, which generates
a processed baseband signal by filtering, decoding, and/or
digitizing the baseband or IF signal. The RX processing circuitry
325 transmits the processed baseband signal to the speaker 330
(such as for voice data) or to the processor 340 for further
processing (such as for web browsing data).
[0066] The TX processing circuitry 315 receives analog or digital
voice data from the microphone 320 or other outgoing baseband data
(such as web data, e-mail, or interactive video game data) from the
processor 340. The TX processing circuitry 315 encodes,
multiplexes, and/or digitizes the outgoing baseband data to
generate a processed baseband or IF signal. The RF transceiver 310
receives the outgoing processed baseband or IF signal from the TX
processing circuitry 315 and up-converts the baseband or IF signal
to an RF signal that is transmitted via the antenna 305.
[0067] The processor 340 can include one or more processors or
other processing devices and execute the OS 361 stored in the
memory 360 in order to control the overall operation of the UE 116.
For example, the processor 340 could control the reception of
forward channel signals and the transmission of reverse channel
signals by the RF transceiver 310, the RX processing circuitry 325,
and the TX processing circuitry 315 in accordance with well-known
principles. In some embodiments, the processor 340 includes at
least one microprocessor or microcontroller.
[0068] The processor 340 is also capable of executing other
processes and programs resident in the memory 360, such as
processes for UL transmission on uplink channel. The processor 340
can move data into or out of the memory 360 as required by an
executing process. In some embodiments, the processor 340 is
configured to execute the applications 362 based on the OS 361 or
in response to signals received from gNBs or an operator. The
processor 340 is also coupled to the I/O interface 345, which
provides the UE 116 with the ability to connect to other devices,
such as laptop computers and handheld computers. The I/O interface
345 is the communication path between these accessories and the
processor 340.
[0069] The processor 340 is also coupled to the touchscreen 350 and
the display 355. The operator of the UE 116 can use the touchscreen
350 to enter data into the UE 116. The display 355 may be a liquid
crystal display, light emitting diode display, or other display
capable of rendering text and/or at least limited graphics, such as
from web sites.
[0070] The memory 360 is coupled to the processor 340. Part of the
memory 360 could include a random access memory (RAM), and another
part of the memory 360 could include a Flash memory or other
read-only memory (ROM).
[0071] Although FIG. 3 illustrates one example of UE 116, various
changes may be made to FIG. 3. For example, various components in
FIG. 3 could be combined, further subdivided, or omitted and
additional components could be added according to particular needs.
As a particular example, the processor 340 could be divided into
multiple processors, such as one or more central processing units
(CPUs) and one or more graphics processing units (GPUs). Also,
while FIG. 3 illustrates the UE 116 configured as a mobile
telephone or smartphone, UEs could be configured to operate as
other types of mobile or stationary devices.
[0072] FIG. 4A is a high-level diagram of transmit path circuitry.
For example, the transmit path circuitry may be used for an
orthogonal frequency division multiple access (OFDMA)
communication. FIG. 4B is a high-level diagram of receive path
circuitry. For example, the receive path circuitry may be used for
an orthogonal frequency division multiple access (OFDMA)
communication. In FIGS. 4A and 4B, for downlink communication, the
transmit path circuitry may be implemented in a base station (gNB)
102 or a relay station, and the receive path circuitry may be
implemented in a user equipment (e.g., user equipment 116 of FIG.
1). In other examples, for uplink communication, the receive path
circuitry 450 may be implemented in a base station (e.g., gNB 102
of FIG. 1) or a relay station, and the transmit path circuitry may
be implemented in a user equipment (e.g., user equipment 116 of
FIG. 1).
[0073] Transmit path circuitry comprises channel coding and
modulation block 405, serial-to-parallel (S-to-P) block 410, Size N
Inverse Fast Fourier Transform (IFFT) block 415, parallel-to-serial
(P-to-S) block 420, add cyclic prefix block 425, and up-converter
(UC) 430. Receive path circuitry 450 comprises down-converter (DC)
455, remove cyclic prefix block 460, serial-to-parallel (S-to-P)
block 465, Size N Fast Fourier Transform (FFT) block 470,
parallel-to-serial (P-to-S) block 475, and channel decoding and
demodulation block 480.
[0074] At least some of the components in FIGS. 4A 400 and 4B 450
may be implemented in software, while other components may be
implemented by configurable hardware or a mixture of software and
configurable hardware. In particular, it is noted that the FFT
blocks and the IFFT blocks described in this disclosure document
may be implemented as configurable software algorithms, where the
value of Size N may be modified according to the
implementation.
[0075] Furthermore, although this disclosure is directed to an
embodiment that implements the Fast Fourier Transform and the
Inverse Fast Fourier Transform, this is by way of illustration only
and may not be construed to limit the scope of the disclosure. It
may be appreciated that in an alternate embodiment of the present
disclosure, the Fast Fourier Transform functions and the Inverse
Fast Fourier Transform functions may easily be replaced by discrete
Fourier transform (DFT) functions and inverse discrete Fourier
transform (IDFT) functions, respectively. It may be appreciated
that for DFT and IDFT functions, the value of the N variable may be
any integer number (i.e., 1, 4, 3, 4, etc.), while for FFT and IFFT
functions, the value of the N variable may be any integer number
that is a power of two (i.e., 1, 2, 4, 8, 16, etc.).
[0076] In transmit path circuitry 400, channel coding and
modulation block 405 receives a set of information bits, applies
coding (e.g., LDPC coding) and modulates (e.g., quadrature phase
shift keying (QPSK) or quadrature amplitude modulation (QAM)) the
input bits to produce a sequence of frequency-domain modulation
symbols. Serial-to-parallel block 410 converts (i.e.,
de-multiplexes) the serial modulated symbols to parallel data to
produce N parallel symbol streams where N is the IFFT/FFT size used
in BS 102 and UE 116. Size N IFFT block 415 then performs an IFFT
operation on the N parallel symbol streams to produce time-domain
output signals. Parallel-to-serial block 420 converts (i.e.,
multiplexes) the parallel time-domain output symbols from Size N
IFFT block 415 to produce a serial time-domain signal. Add cyclic
prefix block 425 then inserts a cyclic prefix to the time-domain
signal. Finally, up-converter 430 modulates (i.e., up-converts) the
output of add cyclic prefix block 425 to RF frequency for
transmission via a wireless channel. The signal may also be
filtered at baseband before conversion to RF frequency.
[0077] The transmitted RF signal arrives at the UE 116 after
passing through the wireless channel, and reverse operations to
those at gNB 102 are performed. Down-converter 455 down-converts
the received signal to baseband frequency, and remove cyclic prefix
block 460 removes the cyclic prefix to produce the serial
time-domain baseband signal. Serial-to-parallel block 465 converts
the time-domain baseband signal to parallel time-domain signals.
Size N FFT block 470 then performs an FFT algorithm to produce N
parallel frequency-domain signals. Parallel-to-serial block 475
converts the parallel frequency-domain signals to a sequence of
modulated data symbols. Channel decoding and demodulation block 480
demodulates and then decodes the modulated symbols to recover the
original input data stream.
[0078] Each of gNBs 101-103 may implement a transmit path that is
analogous to transmitting in the downlink to user equipment 111-116
and may implement a receive path that is analogous to receiving in
the uplink from user equipment 111-116. Similarly, each one of user
equipment 111-116 may implement a transmit path corresponding to
the architecture for transmitting in the uplink to gNBs 101-103 and
may implement a receive path corresponding to the architecture for
receiving in the downlink from gNBs 101-103.
[0079] 5G communication system use cases have been identified and
described. Those use cases can be roughly categorized into three
different groups. In one example, enhanced mobile broadband (eMBB)
is determined to do with high bits/sec requirement, with less
stringent latency and reliability requirements. In another example,
ultra reliable and low latency (URLL) is determined with less
stringent bits/sec requirement. In yet another example, massive
machine type communication (mMTC) is determined that a number of
devices can be as many as 100,000 to 1 million per km2, but the
reliability/throughput/latency requirement could be less stringent.
This scenario may also involve power efficiency requirement as
well, in that the battery consumption may be minimized as
possible.
[0080] A communication system includes a downlink (DL) that conveys
signals from transmission points such as base stations (BSs) or
NodeBs to user equipments (UEs) and an Uplink (UL) that conveys
signals from UEs to reception points such as NodeBs. A UE, also
commonly referred to as a terminal or a mobile station, may be
fixed or mobile and may be a cellular phone, a personal computer
device, or an automated device. An eNodeB, which is generally a
fixed station, may also be referred to as an access point or other
equivalent terminology. For LTE systems, a NodeB is often referred
as an eNodeB.
[0081] In a communication system, such as LTE system, DL signals
can include data signals conveying information content, control
signals conveying DL control information (DCI), and reference
signals (RS) that are also known as pilot signals. An eNodeB
transmits data information through a physical DL shared channel
(PDSCH). An eNodeB transmits DCI through a physical DL control
channel (PDCCH) or an Enhanced PDCCH (EPDCCH).
[0082] An eNodeB transmits acknowledgement (ACK) information in
response to data transport block (TB) transmission from a UE in a
physical hybrid ARQ indicator channel (PHICH). An eNodeB transmits
one or more of multiple types of RS including a UE-common RS (CRS),
a channel state information RS (CSI-RS), or a demodulation RS
(DMRS). A CRS is transmitted over a DL system bandwidth (BW) and
can be used by UEs to obtain a channel estimate to demodulate data
or control information or to perform measurements. To reduce CRS
overhead, an eNodeB may transmit a CSI-RS with a smaller density in
the time and/or frequency domain than a CRS. DMRS can be
transmitted only in the BW of a respective PDSCH or EPDCCH and a UE
can use the DMRS to demodulate data or control information in a
PDSCH or an EPDCCH, respectively. A transmission time interval for
DL channels is referred to as a subframe and can have, for example,
duration of 1 millisecond.
[0083] DL signals also include transmission of a logical channel
that carries system control information. A BCCH is mapped to either
a transport channel referred to as a broadcast channel (BCH) when
the DL signals convey a master information block (MIB) or to a DL
shared channel (DL-SCH) when the DL signals convey a System
Information Block (SIB). Most system information is included in
different SIBs that are transmitted using DL-SCH. A presence of
system information on a DL-SCH in a subframe can be indicated by a
transmission of a corresponding PDCCH conveying a codeword with a
cyclic redundancy check (CRC) scrambled with system information
RNTI (SI-RNTI). Alternatively, scheduling information for a SIB
transmission can be provided in an earlier SIB and scheduling
information for the first SIB (SIB-1) can be provided by the
MIB.
[0084] DL resource allocation is performed in a unit of subframe
and a group of physical resource blocks (PRBs). A transmission BW
includes frequency resource units referred to as resource blocks
(RBs). Each RB includes N.sub.sc.sup.RB sub-carriers, or resource
elements (REs), such as 12 REs. A unit of one RB over one subframe
is referred to as a PRB. A UE can be allocated M.sub.PDSCH RBs for
a total of M.sub.sc.sup.PDSCH=M.sub.PDSCHN.sub.sc.sup.RB REs for
the PDSCH transmission BW.
[0085] UL signals can include data signals conveying data
information, control signals conveying UL control information
(UCI), and UL RS. UL RS includes DMRS and Sounding RS (SRS). A UE
transmits DMRS only in a BW of a respective PUSCH or PUCCH. An
eNodeB can use a DMRS to demodulate data signals or UCI signals. A
UE transmits SRS to provide an eNodeB with an UL CSI. A UE
transmits data information or UCI through a respective physical UL
shared channel (PUSCH) or a Physical UL control channel (PUCCH). If
a UE needs to transmit data information and UCI in a same UL
subframe, the UE may multiplex both in a PUSCH. UCI includes Hybrid
Automatic Repeat request acknowledgement (HARQ-ACK) information,
indicating correct (ACK) or incorrect (NACK) detection for a data
TB in a PDSCH or absence of a PDCCH detection (DTX), scheduling
request (SR) indicating whether a UE has data in the UE's buffer,
rank indicator (RI), and channel state information (CSI) enabling
an eNodeB to perform link adaptation for PDSCH transmissions to a
UE. HARQ-ACK information is also transmitted by a UE in response to
a detection of a PDCCH/EPDCCH indicating a release of
semi-persistently scheduled PDSCH.
[0086] An UL subframe includes two slots. Each slot includes
N.sub.symb.sup.UL symbols for transmitting data information, UCI,
DMRS, or SRS. A frequency resource unit of an UL system BW is a RB.
A UE is allocated N.sub.RB RBs for a total of
N.sub.RBN.sub.sc.sup.RB REs for a transmission BW. For a PUCCH,
N.sub.RB=1. A last subframe symbol can be used to multiplex SRS
transmissions from one or more UEs. A number of subframe symbols
that are available for data/UCI/DMRS transmission is
N.sub.symb=2(N.sub.symb.sup.UL-1)-N.sub.SRS, where N.sub.SRS=1 if a
last subframe symbol is used to transmit SRS and N.sub.SRS=0
otherwise.
[0087] FIGS. 5A-D illustrate base station configuration parameters
and corresponding effect on a received power heat map according to
embodiments of the present disclosure. The embodiments of the base
station configuration parameters and corresponding effect on a
received power heat map shown in FIGS. 5A-D are for illustration
only and other embodiments can be used without departing from the
scope of the present disclosure.
[0088] The configuration of a gNB 102 can involve many different
parameters, such as their location, antenna height, antenna
pattern, mechanical tilt (M-tilt), electrical tilt (E-tilt),
azimuth angle, and the like. As illustrated in FIG. 5A-D, these
parameters may impact the coverage pattern of a BS significantly.
For example, FIG. 5B illustrates a received power heat map 515 when
an azimuth .theta.=0.degree.; FIG. 5C illustrates a received power
heat map 520 when an azimuth .theta.=55.degree. and M-tilt
.phi.=9.degree.; and FIG. 5D illustrates a received power heat map
525 when an azimuth .theta.=55.degree. and M-tilt
.phi.=3.degree..
[0089] Consequently, referring again to FIG. 1, the parameters of
gNB 102 may substantially impact the service quality to UEs
111-116. An incorrectly chosen set of these parameters can degrade
network coverage and cause a plethora of issues, such as coverage
islands, coverage holes, cell overshoot problems, and so forth.
Therefore, significant effort is spent in network planning and
optimization to determine an optimal choice of these parameters
prior to installing BSs. Some of these parameters may also be
reconfigurable, such as the azimuth angle .theta. 505, E-tilt,
M-tilt .phi. 510, and the like, and may be changed by the network
service provider to adapt to a changing radio-frequency (RF)
environment. The reconfiguration of azimuth angle .theta. 505 and
M-tilt .phi. 510, in particular, may require intervention by a site
engineer and may be prone to human error. Examples of such errors
may include misalignment with the desired angle, swapping of
antenna ports, use of uncalibrated measurement equipment, and so
forth. Environmental conditions like wind, earthquakes, birds, and
the like, can also impact the physical orientation of the antenna
affecting these parameters over time. Finally, since these BS
parameters are stored in a database, the BS parameters may also be
prone to book-keeping errors. While misaligned BS parameters may
degrade performance, book-keeping errors may lead to incorrect
estimates of network performance and can adversely affect many
self-organization network (SON) applications. Thus, due to the
critical impact of BS parameters on network performance, a
mechanism may be required to estimate the currently configured set
of BS parameters. This task of predicting and correcting the BS
configuration parameters is often referred to as the site audit
correction problem.
[0090] FIG. 6 illustrates a site audit process according to
embodiments of the present disclosure. While the flow chart depicts
a series of sequential steps, unless explicitly stated, no
inference should be drawn from that sequence regarding specific
order of performance, performance of steps or portions thereof
serially rather than concurrently or in an overlapping manner, or
performance of the steps depicted exclusively without the
occurrence of intervening or intermediate steps. The process
depicted in the example depicted is implemented by a transmitter
and processor circuitry in, for example, a respective UE, core
network entity, and base station. Process 600 can be accomplished
by, for example, UE 116, gNB 102, and CNE 135 in network 100. The
different operations and associated embodiments are described in
more detail with respect to FIGS. 7-15.
[0091] The gNB 102 periodically transmits a reference signal (RS)
605 and also provides a communication link to the UE 116 and the
CNE 135. The UE 116 receives the RS and measures the reference
signal received power (RSRP) in block 610. In certain embodiments,
the UE 116 includes additional sensors that are configured to
obtain other information including an estimate of a location of the
UE 116, as shown in block 610. In block 615, the UE 116
periodically encodes all this information and RSRP values into
measurement reports. In response to a query, the UE 116 transmits
the measurement reports to the CNE 135. In certain embodiments, the
UE 116 receives the query from the CNE 135 directly or via the gNB
102 in block 620. In certain embodiments, the UE 116 receives the
query from the gNB 102 in block 620. In certain embodiments, the UE
116 transmits the measurement reports directly to the CNE 135, via
the gNB 102 in block 620, or via an alternate route.
[0092] The CNE 135 is responsible for the site audit process and
correction or remedial measures. In response an external trigger
625, the CNE 135 initiates the audit process for a target gNB 102
by fetching the book-values and side information on the BS
configuration parameters in block 630. That is, in block 630, the
CNE 135 fetches current book values and BS configuration
parameters. In block 635, based on the trigger 625 and fetched
data, CNE 135 determines the BS parameters to estimate. In block
640, CNE 135 fetches measurement reports from one or more UEs
111-116 in a vicinity of gNB 102. Then, in block 645 CNE 130 runs
the site audit algorithm 645 to obtain a result of the audit
process. Based on the result, in block 650, the CNE 130 takes
appropriate action or remedial measure as shall be discussed in
more detail with respect to FIGS. 7-15. The site audit algorithm
645 includes several steps, including preprocessing of user reports
645a, determining which of a plethora of available algorithms to
run 645b and running of the picked algorithms 645c, post-processing
the results from the picked algorithms 645d.
[0093] In certain embodiments, gNB 102 broadcasts a reference
signal (RS) to enable users in its neighborhood to measure the
signal strength via an RSRP measurement. This RS may be an existing
RS present in the 3GPP framework or can be a new RS transmitted
with a pre-determined beam shape. The RS may either be broadcast
periodically or may be triggered by a network condition. Both
served UEs 111-114 and non-served UEs 115-116 in the neighborhood
of gNB 102 may correspondingly measure the RSRP from gNB 102. The
UEs 111-118 may be network subscribers or may be agents deployed by
the service provider, such as to operate as RF scanners. The UEs
111-118 may be equipped with a global positioning system (GPS),
altimeter, accelerometer gyroscope, and the like, and may
periodically measure their location estimate, altitude,
orientation, and so forth. Thus over time, UE 116 can create and
save measurement reports, containing: (i) the time stamp of the
report, (ii) RSRP for the serving gNB 103, (iii) RSRP from a
neighboring gNB 102, (iv) the physical cell identifier (PCI) for
the corresponding serving and neighboring BSs, (v) an estimate of
the UE location, (vi) altitude of the UE, (vii) an indicator of the
accuracy of the user location, (viii) orientation of the UE, (ix) a
flag indicating the connectivity to a WiFi service, (x) an
identifier for the make/model of the UE, (xi) timing advance
configured for the UE, and the like. Over a period of time, several
such reports can be collected and saved, and the entries from these
measurement reports may also be deleted by the UE after an
expiration time.
[0094] The CNE 135 is responsible for predicting and correcting the
configuration parameters of BSs within its service area. In certain
embodiments, the CNE 135 is a base station, such as gNB 103 itself.
In certain embodiments, the CNE 135 initiates the audit process for
a target BS, gNB 103 based on an external trigger. An example for
such a trigger can be expiration of a timer, the output of a root
cause analysis algorithm, and the like. In certain embodiments, the
CNE 135 also maintains a database of configuration parameters of
the BSs in its service area. In certain embodiments, the CNE 135
queries the target gNB 103 in order to obtain this information.
Some of the parameters in the database may be inaccurate or
missing.
[0095] Based on the trigger and the fetched configuration data, the
CNE 135 determines the set of BS parameters to estimate or correct.
In order to predict the determined parameters of a target gNB 103,
the CNE 135 can collect and pool the measurement reports from
terminals in the neighborhood of gNB 103. For example, the CNE 135
can collect and pool the measurement reports from one or more of
UEs 115-118. In certain embodiments, the CNE 135 can collect and
pool the measurement reports from a select UEs of UEs 115-118 that
meet specified criteria. As shown in the example illustrated in
FIG. 1, these terminals may also include terminals, such as UEs
115-116, associated with a neighboring gNB 102. The transmission of
the reports to CNE 135 may be triggered periodically by a timer, by
a specific UE condition, or may be triggered by gNB 103 or CNE 135
via a signaling message. The signaling message may also include a
list of attributes to report and conditions for a UE to be eligible
for reporting. For example, the CNE 135 may already have pooled
several reports and may only desire reports from a specific
critical geographic location.
[0096] In certain embodiments, the measurement reports from UE 118
is first transmitted to gNB 103 via a cellular link. The serving
gNB 103 may collect multiple such reports, process the reports, and
then forward to the CNE 135 via a backhaul link, which can be wired
or wireless. In certain embodiments, the reports are collected by
the CNE 135 via an alternate mechanism, such as via a Wi-Fi
service. A user may periodically measure and create the user
reports or may initiate measurement upon being triggered by the
serving gNB 103. In order to predict the parameters for a target
gNB 103, the CNE 135 pools the measurement reports from UEs
115-118, associated with target gNB 103 as well as from UEs
associated with neighboring gNB 102, which are UEs 111-114.
[0097] FIG. 7 illustrates a process for generation and collection
of measurement reports by a user equipment according to embodiments
of the present disclosure. While the flow chart depicts a series of
sequential steps, unless explicitly stated, no inference should be
drawn from that sequence regarding specific order of performance,
performance of steps or portions thereof serially rather than
concurrently or in an overlapping manner, or performance of the
steps depicted exclusively without the occurrence of intervening or
intermediate steps. The process depicted in the example depicted is
implemented by a transmitter or processor chain in, for example, a
UE. Process 700 can be accomplished by, for example, UE 111-118 in
network 100.
[0098] A user device, such as UE 116, receives a trigger 705 to
start RSRP measurements. The trigger 705 can be the successful
decoding of a reference signal, a timing trigger, and the like. In
block 710, UE 116 measures the RSRP values for the received
reference signal, and may also acquire other information such as BS
Physical Cell Identifier (PCI). In block 715, UE 116 can accumulate
the RSRP values for nearby cells, along with other parameters such
as location estimates, and the like as discussed herein above, in a
measurement report. In block 720, UE 116 deletes one or more
entries from the measurement report. For example, UE 116 may delete
an entry from the measurement report in response to the expiration
of a timer or if a predetermined time period has elapsed since the
entry was recorded. Additionally, UE 116 may delete or modify an
entry from the measurement report in response to when new updated
values of measurements are available. In block 725, UE 116 receives
a signaling message or a trigger to transmit user reports. In block
725, UE 116 also checks to determine whether UE 116 is eligible for
reporting. Upon eligibility, UE 116 forwards (transmits) the
measurement report to the CNE 135 in block 730. In certain
embodiments, UE 116 transmits the measurement report via the parent
BS, gNB 103. In certain embodiments, UE 116 transmits the
measurement report through an alternate route, such as via WiFi
service. In certain embodiments, UE 116 transmits the measurement
report directly to the CNE 135.
[0099] FIG. 8 illustrates a process for generation and collection
of measurement reports by a base station according to embodiments
of the present disclosure. While the flow chart depicts a series of
sequential steps, unless explicitly stated, no inference should be
drawn from that sequence regarding specific order of performance,
performance of steps or portions thereof serially rather than
concurrently or in an overlapping manner, or performance of the
steps depicted exclusively without the occurrence of intervening or
intermediate steps. The process depicted in the example depicted is
implemented by a transmitter or processor chain in, for example, a
BS. Process 800 can be accomplished by, for example, gNB 102 or gNB
103 in network 100.
[0100] The BS, such as gNB 103, may receive a timing or other
trigger 805 to initiate new RS transmission. In block 810, gNB 103
can broadcast a reference signal to enable RSRP measurements. In
block 815, in certain embodiments, gNB 103 receives a trigger from
the CNE 135 to fetch measurement reports. In certain embodiments,
gNB 103 may correspondingly send a request to its UEs, such as UEs
115-118. In block 820, in certain embodiments, gNB 103 collects the
measurement reports from the served UEs, namely, one or more of UEs
115-118. In block 825, in certain embodiments, gNB 103 processes
the collected user measurement reports. In block 830, gNB 103
forwards the processed measurement report and/or book values of BS
configuration parameters and any side information to the core
network entity via the backhaul connections.
[0101] FIGS. 9 and 10 illustrate a model-based base station
parameter estimation according to embodiments of the present
disclosure. The embodiment of the parameter estimation shown in
FIGS. 9 and 10 is for illustration only and other embodiments could
be used without departing from the scope of the present
disclosure.
[0102] After collecting the measurement reports 905 associated with
a target gNB 103, the CNE 135 may undertake a preprocessing task to
filter out faulty, corrupted or unwanted measurement reports. In
certain embodiments, the CNE 135 also has access to a RSRP
prediction tool 910 for estimating the RSRP heat map from gNB 103
to its surrounding area, such as heat maps 515, 520, and 525 in
FIGS. 5B-5D. Examples of such tools include: wireless ray-tracing,
statistical channel models, distance-dependent pathloss equations,
and the like. The CNE 135 determines and executes an audit
algorithm 915 to generate estimated site parameters 920. The audit
algorithm 915 compares an expected strong RSRP region 1005 from the
RSRP prediction tool 910 against a user distribution 1010 for
strong RSRPs from the UE reports 905. By comparing the RSRP values
in user measurement reports to the RSRP values in the tool's 910
heat map, the CNE 135 predicts correct values, that is estimated
site parameters 920, of the error-prone BS configuration
parameters.
[0103] FIGS. 11 and 12 illustrate a model-free base station
parameter estimation according to embodiments of the present
disclosure. The embodiment of the parameter estimation shown in
FIGS. 9 and 10 is for illustration only and other embodiments could
be used without departing from the scope of the present
disclosure.
[0104] In certain embodiments, the CNE 135 may not have access to
an RSRP estimation tool 910. In this case, the BS parameters can be
estimated using the UE reports 905 and some generic wireless
propagation laws. Examples of such laws can be: UE reports are
generally more densely distributed in the boresight direction 1205
of the antenna, as shown in heat map 1200, UE reported RSRPs are
typically higher near the boresight direction 1205 of the
antenna.
[0105] Many model and model-free algorithms 1010 are possible for
such base station parameter estimation, and a few illustrative
solutions are discussed in the following subsections. In certain
embodiments, the CNE 135 has access to many such estimation
algorithms 1010 and can first determine a subset of algorithms to
execute to obtain estimated site parameters 1115. Such a
determination can be based on: side information, BS parameters to
estimate, the trigger initiating the site audit process etc. As an
example, if the RSRP prediction tool 910 results are known to be
inaccurate for a certain BS, the CNE 135 can decide to use the
model-free estimation approach for that BS. In certain embodiments,
the CNE 135 runs a first estimation algorithm, and based on the
result and some logic, may determine to run additional estimation
algorithms.
[0106] In certain embodiments, the estimated site parameters 915 or
1115 output from the model-based or model-free estimation may
directly be the error-prone BS configuration parameters. In certain
embodiments, the estimated site parameters 915 or 1115 output may
be a likelihood score for the different realizations of the BS
parameters. In certain embodiments, the estimated site parameters
915 or 1115 output may be an image of the predicted RSRP or
coverage pattern around the BS.
[0107] Referring back to FIG. 1, in one example involving
model-based estimation, the set of preprocessed measurement reports
collected by the network entity 106 for a particular BS 102a is
represented by: {RSRP.sub.u, LOC.sub.u|u U}. Here U is the set of
the users that transmitted the reports and RSRP.sub.u, LOC.sub.u
represent the RSRP received by user u from gNB 103 and the location
estimate of the user terminal u, respectively. While the estimation
approaches are applicable for any BS parameters, for ease of
illustration, in this example, error prone BS parameters to be
estimated are restricted to: azimuth angle .theta. and the
mechanical down-tilt .phi.. The CNE 135 also has access to a tool
for generating RSRP prediction in the area surrounding gNB 103. For
any location LOC and given angle hypothesis: {{circumflex over
(.theta.)}, {circumflex over (.phi.)}} this predicted RSRP value is
defined as (LOC, {circumflex over (.theta.)}, {circumflex over
(.phi.)}, h). Here h is a vector of tool parameters whose accurate
values may be unknown apriori, such as the pathloss exponent,
reflection loss, and the like. For example, when h=[h.sub.0,
h.sub.1] includes a pathloss exponent error h.sub.0 and bias error
h.sub.1:
(LOC,{circumflex over (.theta.)},{circumflex over
(.phi.)},h)=(LOC,{circumflex over (.theta.)},{circumflex over
(.phi.)})+10h.sub.0 log d(LOC)+h.sub.1, (1)
where d(LOC) is the distance of LOC from the BS in meters. The BS
parameter estimation problem can be formulated as:
.theta. , .phi. = arg .times. min .theta. ^ , .phi. ^ , h .times. {
.function. [ { RSRP u , .times. ( LOC u , .theta. ^ , .phi. ^ , h )
| u .di-elect cons. } ] } , ( 2 ) ##EQU00001##
where ( , ) is a loss function that quantifies the gap between the
observed RSRPs and the RSRP tool predictions for a given angle
hypothesis {circumflex over (.theta.)}, {circumflex over (.phi.)}.
Some examples of ( , ) include the mean-squared error, correlation
coefficient, etc. The search over {circumflex over
(.theta.)},{circumflex over (.phi.)}, h in the above equation can
either be performed exhaustively, sequentially, iteratively or
hierarchically. In certain embodiments, where while the minimizing
argument may yield the angle estimates, the value of the loss at
the minima may represent the likelihood score of the estimates.
Such a likelihood may be utilized by the network entity to
determine, for example, whether to update the parameter database or
deploy a network engineer to physically check the BS
parameters.
[0108] Parameter estimation with location error: In one embodiment
the site audit algorithm is capable of accommodating for location
errors in the user reports as discussed below.
[0109] FIG. 13 illustrates location errors in user reports
according to embodiments of the present disclosure. The embodiment
of the location errors in user reports shown in FIG. 13 is for
illustration only and other embodiments could be used without
departing from the scope of the present disclosure.
[0110] While the estimation approaches are applicable for any BS
parameter, for ease of illustration, examples disclosed herein
restrict error prone BS parameters to estimate to: azimuth angle
.theta. and the mechanical down-tilt .phi.. The reported locations
LOC.sub.u 1305 in the reports {RSRP.sub.u, LOC.sub.u|u } may often
be faulty, due to for example GPS errors. As an example, the true
location LOC.sub.u 1310 may lie in a ball of radius r around the
reported location LOC.sub.u 1305, illustrated as (LOC.sub.u) 1315.
To account for such errors, the estimation algorithms can be
reformulated. As an example, for the mean square error loss
function we can have:
.theta. , .phi. = argmin .theta. ^ , .phi. ^ , h .times. { u
.di-elect cons. .times. min LOC .times. .times. .function. ( LOC u
) .times. [ RSRP u - .times. ( LOC , .theta. ^ , .phi. ^ , h ) ] 2
.times. / .times. } . ( 3 ) ##EQU00002##
[0111] Several other variations are also possible for combining the
RSRP estimates within (LOC.sub.u) 1305 during the computation of
the loss function. For example, in an embodiment where prior
knowledge of user locations or location densities is available, the
reported locations can be corrected before estimation as:
.theta. , .phi. = argmin .theta. ^ , .phi. ^ , h .times. {
.function. [ { RSRP u , .times. ( .function. ( LOC u ) , .theta. ^
, .phi. ^ , h ) | u .di-elect cons. } ] } , ( 4 ) ##EQU00003##
where ( ) is a location mapping function.
[0112] Parameter estimation with user weighting: In one embodiment
of the site audit algorithm, the algorithm is capable of weighting
different user reports appropriately as described below.
[0113] While the estimation approaches are applicable for any BS
parameters, for ease of illustration, examples disclosed herein
restrict error prone BS parameters to estimate to: azimuth angle
.theta. and the mechanical down-tilt .phi.. In some embodiments,
the accuracy of the user measurement reports may be different for
different users. For example, location estimates may be more
accurate for users in outdoor open areas, and with more
sophisticated user devices. Similarly, the modeling accuracy of the
RSRP prediction tool may be different for different regions. For
example, it may be easier to model propagation in line-of-sight
(LoS) regions than non-line-of-sight (nLoS) regions. Consequently,
during the estimation process a higher weightage w.sub.u may be
assigned to users with accurate models and/or less errors than
others. As an example, the estimation process with the mean square
error loss can be reformulated as:
.theta. , .phi. = argmin .theta. ^ , .phi. ^ , h .times. { u
.di-elect cons. .times. w u .function. [ RSRP u - .times. ( LOC u ,
.theta. ^ , .phi. ^ , h ) ] 2 .times. / .times. } . ( 5 )
##EQU00004##
[0114] In certain embodiments, a higher weightage can also be given
to users in certain strategic locations, such as known high user
density areas, or users for which the target BS is the serving
BS.
[0115] Parameter estimation with side information: In one
embodiment of the site audit algorithm, the algorithm is capable of
exploiting side information or contextual information about the BS
and the user reports as described next.
[0116] FIG. 14 illustrates a cell association boundary based
parameter estimation according to embodiments of the present
disclosure. The embodiment of cell association boundary based
parameter estimation shown in FIG. 14 is for illustration only and
other embodiments could be used without departing from the scope of
the present disclosure.
[0117] While the estimation approaches are applicable for any BS
parameters, for ease of illustration, examples disclosed herein
restrict error prone BS parameters to estimate to: azimuth angle
.theta. and the mechanical down-tilt .phi.. In certain embodiments,
additional side information may be available at the CNE 135, such
as an estimate of the cell boundary 1405 of the target gNB 103,
apriori knowledge of user locations, previous estimates of the BS
parameters, and the like. For example, a cell boundary 1405 can be
estimated with knowledge of the configuration parameters for the
BSs in the neighborhood of the target gNB 103, and by using the
RSRP prediction tool 910 to predict RSRP from each of them. As
another example, for each hypothesis of BS configuration parameters
the cell boundary shape can be approximated as a sectoral region.
In one embodiment, to exploit such cell association information,
the estimation process can be reformulated as:
.theta. , .phi. = argmin .theta. ^ , .phi. ^ , h .times. .times. {
.function. [ { RSRP u , .times. ( LOC u , .theta. ^ , .phi. ^ , h )
| u .di-elect cons. } ] + .function. ( { u | u .di-elect cons.
.function. ( .theta. ^ , .phi. ^ ) } ) } , ( 6 ) ##EQU00005##
where ( , ) is a loss function that quantifies the gap between the
observed RSRPs and the raytracing predictions for a given angle
hypothesis {circumflex over (.theta.)}, {circumflex over (.phi.)},
({circumflex over (.theta.)},{circumflex over (.phi.)}) is the
subset of users 1410 whose serving cell is the target gNB 103 but
whose locations lie outside the cell boundary 1405 predicted by the
RSRP prediction tool 910, and ( ) is an associated penalty. In
certain embodiments in which the RSRP prediction tool 910 is
unavailable, a loss function ( , ) can be bypassed and the
parameter estimation can be performed only using the cell
association penalty ( ). This embodiment can also be an example of
model-free estimation.
[0118] FIG. 15 illustrates deep learning based parameter estimation
according to embodiments of the present disclosure. The embodiment
of the deep learning based parameter estimation 1500 shown in FIG.
15 is for illustration only and other embodiments could be used
without departing from the scope of the present disclosure.
[0119] While the estimation approaches are applicable for any BS
parameters, for ease of illustration, examples disclosed herein
restrict error prone BS parameters to estimate to: azimuth angle
.theta. and the mechanical down-tilt .phi.. In certain embodiments,
the estimation of the correct BS configuration parameters may be
performed using a neural network 1500. In certain embodiments, the
measurement reports can be encoded as an image 1505, where the
pixel coordinates 1510 represent location and pixel color or
shading represents RSRP value. The neural network 1500 can then
estimate the correct BS configuration parameter using the
measurement image 1505 as an input, that is, an image
classification task. A large number of real or simulated
measurement reports can be collected to train the neural network
1500 to perform this task. In certain embodiments, the score
function 1515 returned by the neural network 1500 can be a measure
of the estimation accuracy and can be used to decide further
actions to take. In certain embodiments, side information such as
the reliability of the user reports, apriori estimates of the
parameters, and the like, can be fed as additional input channels
to the network.
[0120] In certain embodiments, the BS configuration parameters can
be modeled as a time evolving random process. Such a time evolution
can, for example, be modelled using a state transition model or a
Markov process. Note that the state of a BS may include some of the
BS configuration parameters along with some other hidden
parameters. In certain embodiments, the CNE 135 stores the book
values of the target BS parameters as well as a last estimate of
the BS state. The CNE 135 can then use the past state estimate of
the BS configuration as side information during the estimation
process with new user reports. Example methods for performing such
estimation include maximum apriori probability estimation with a
state dependent prior, Kalman filtering, particle filter, partially
observed Markov process, and the like.
[0121] In certain embodiments, a Bayesian filtering is used, of
which Kalman and Particle filtering are special cases. The general
Bayes filter algorithm that is used for tracking is given below.
Here X.sub.t is the state of the system (like BS azimuth and tilt)
at time t, U.sub.t are the controls (for instance if there is a
planned action to change the state) and Z.sub.t are measurements
(for instance UE measurement reports or UE locations). The belief
of the system to be in state X is given as bel(X). Conditional
probability of event A given event B is denoted as p(A|B). Note
that bel(X)=p(X.sub.t|X.sub.t-1,U.sub.t,Z.sub.t).
TABLE-US-00001 Algorithm Bayes_filter(bel(X.sub.t-1), U.sub.t,
Z.sub.t) for all X.sub.t do bel(X.sub.t) = .intg.
p(X.sub.t|U.sub.t, X.sub.t-1)bel(X.sub.t-1)dX.sub.t-1 bel(X.sub.t)
= .eta.p(Z.sub.t|X.sub.t)bel(X.sub.t) endfor return
bel(X.sub.t)
[0122] Note that .eta. is a normalizing term to make bel(X.sub.t)
as a valid probability distribution. In order to implement this
algorithm, which could be done using popular Kalman filtering or
the particle filtering approach, models can be used that drive
knowledge of p(Z.sub.t|X.sub.t), which comes from a measurement
model, and p(X.sub.t|U.sub.t,X.sub.t-1), which comes from a state
transition/motion model.
[0123] Information obtained from UE GPS locations and measurement
reports with known statistics in their error can be used to come up
with knowledge of p(Z.sub.t|X.sub.t) and
p(X.sub.t|U.sub.t,X.sub.t-1). In certain embodiments, ray tracing
information is used to aid estimating these conditional
probabilities.
[0124] Since the BS azimuth and tilt are expected to not change
with time, the following trivial stationary motion model can be
used.
X t = [ .theta. t .PHI. t ] = [ .theta. t - 1 .PHI. t - 1 ] + [ m t
n t ] ( 7 ) ##EQU00006##
where .theta. is the azimuth angle and .phi. is the tilt angle.
Here, m, n are the noise terms, assuming for now to be some
Gaussian with .sigma..sub.m.sup.2 and .sigma..sub.n.sup.2. Usually
std dev values are expected to be very small, such as <0.1
degrees or so. The RSRP UE reports and the coarse UE GPS
coordinates are obtained as the measurement values. Below equation
is approximate connection between UE locations, RSRP values and the
azimuth/tilt angle
[ R 1 R K ] = [ G .function. ( .theta. , .PHI. , U 1 + .alpha. 1 )
.times. f .function. ( U 1 + .alpha. 1 ) .times. .beta. 1 + .gamma.
1 G .function. ( .theta. , .PHI. , U K + .alpha. K ) .times. f
.function. ( U K + .alpha. K ) .times. .beta. K + .gamma. K ] ( 8 )
##EQU00007##
[0125] Equation 8 can be referenced as the measurement model. Here,
f(x) function maps location x R.sup.2 of UE 116 to corresponding
ray tracing RSRP with omni-directional antennas, U.sub.i indicates
UE location measurement, .alpha..sub.1 indicates the error in GPS
location reported for user i and {.beta..sub.i,.gamma..sub.i}
indicates multiplicative and additive randomness that maps ray
tracing estimates to real measurements on left hand side (R.sub.i).
The intuition behind this equation is as follows. On the right hand
side, f(.) gives estimate of RSRP assuming omni-directional
pattern. Multiplying with antenna gain G(.) approximately yields
the RSRP if there were a directional antenna with azimuth .theta.,
tilt .phi. at UE locations given by U.sub.i, for which the measured
values are on the left hand side of the equation. The parameters
{.beta., .gamma.} are key to the equation to allow some additive or
multiplicative noise of ray tracing versus real measurements.
Connecting this formulation back into the conditional probabilities
to estimate in the Bayesian filtering problem,
p(X.sub.t|X.sub.t-1,U.sub.t)=1(X.sub.t=X.sub.t-1). (9)
P(Z.sub.t|X.sub.t)=p(R.sub.1=r.sub.1, . . .
R.sub.K=r.sub.K,U.sub.1=u.sub.1, . . . ,U.sub.K=u.sub.K|X.sub.t)
(10)
[0126] Using the measurement model and Monte Carlo sampling of
random variables {.alpha., .beta., .gamma.} the conditional
probability can be estimated.
[0127] In certain embodiments, in order to simplify the computation
of this conditional probability, the RSRP values and UE locations
are quantized into some discrete sets. For example, RSRP values can
take values between 1 to N, with N being the best RSRP possible and
1 being the worst RSRP. In certain embodiments, the parameters
{.alpha., .beta., .gamma.} can be tuned based on whether UE 116 is
indoor or outdoor is known. For example, the GPS error is higher
for indoor versus outdoor and thus a higher standard deviation of
.alpha. can be chosen for indoor users and a lower standard
deviation can be chosen for outdoor users.
[0128] In certain embodiments, the gNB 103 antenna panel includes
an inertial measurement unit (IMU) installed thereon. Such IMU can
provide knowledge on change in azimuth and tilt as knows its own
orientation with respect to a fixed coordinate frame. Such inputs
from IMU on the knowledge of change in the state X.sub.t can serve
as control input U.sub.t in the above formulation. For example,
letting U.sub.t denote relative change in azimuth and tilt angle
perceived by the IMU, the conditional probability
p(X.sub.t|X.sub.t-1,U.sub.t) is modified as
p .function. ( X t | X t - 1 , U t ) = 1 .times. ( X t = X t - 1 +
U t ) , where .times. .times. U t = [ .DELTA. .times. .times.
.theta. t .DELTA. .times. .times. .PHI. t ] + .zeta.
##EQU00008##
denotes relative change in azimuth and tilt reported by IMU with
.zeta. denoting the noise in IMU measurements.
[0129] Parameter estimation without RSRP prediction tool (model
free): In another embodiment of the site audit algorithm the CNE
can perform the BS parameter estimation without an RSRP prediction
tool, and is referred to as model-free estimation.
[0130] While the estimation approaches are applicable for any BS
parameters, for ease of illustration, examples disclosed herein
restrict error prone BS parameters to estimate to: azimuth angle
.theta. and the mechanical down-tilt .phi.. In certain embodiments
of model free estimation are based on the general observation that
the RSRP is highest near the antenna boresight direction, as
illustrated in FIG. 12. In this case, a soft partition of the
region around gNB 103 can be created for each .theta. and .phi.,
and the median measured RSRP of user reports within each partition
can be computed. This can, for example, reduce the effects of
outliers in the measured data as well as different user report
densities around the base station. Representing median RSRP of
partition {circumflex over (.theta.)}, {circumflex over (.phi.)} as
RSRP.sub.{circumflex over (.theta.)},{circumflex over (.phi.)}, the
parameter estimation problem can be formulated as:
.theta. , .phi. = argmin .theta. ^ , .phi. ^ .times. { ( RSRP _
.theta. ^ , .phi. ^ ) } , ( 11 ) ##EQU00009##
[0131] Several data-driven loss functions ( ) are possible to
optimize performance. In one embodiment, Deep Neural Networks can
also be used to learn a good loss function.
[0132] FIG. 16 illustrates a core network entity operation in site
audit correction according to embodiments of the present
disclosure. While the flow chart depicts a series of sequential
steps, unless explicitly stated, no inference should be drawn from
that sequence regarding specific order of performance, performance
of steps or portions thereof serially rather than concurrently or
in an overlapping manner, or performance of the steps depicted
exclusively without the occurrence of intervening or intermediate
steps. The process depicted in the example depicted is implemented
by a transmitter or processor chain in, for example, a BS. Process
1600 can be accomplished by, for example, CNE 135, gNB 102 or gNB
103 in network 100.
[0133] The CNE 135 can run one or more site audit algorithms
sequentially or in parallel. The CNE 135 can correspondingly post
process the results from multiple algorithms to merge them. By
comparing the final estimation result to the book values of the BS
parameters, the CNE 135 can determine either to update the
parameter database, or can generate an alarm indicating a possible
misconfiguration of the BS. This determination can be based on, for
example, difference in the estimation result from the book values,
based on the likelihood score of the predicted value, based on the
likelihood score of the book value, and the like. The alarm may
further trigger a secondary action such as dispatching a site
engineer to gNB 1035.
[0134] In block 1605, a timer or a certain network condition may
trigger site audit correction task for a target gNB 103. In block
1610 the CNE 135 determines the target gNB 103 for site audit,
based on the trigger. In block 1615, in certain embodiments, the
CNE 135 fetches the book values of the target BS configuration from
either a database or by querying the target gNB 103. In block 1620,
the CNE 135 determines which BS configuration parameters are
error-prone or corrupted are require estimation. In block 1625, the
CNE 135 determines the BSs and the user device eligibility
conditions for transmission of reports. In block 1630, the CNE 135
initiates the measurement report collection by signaling these BSs
and/or user devices associated with gNB 103. In block 1635, in
certain embodiments, the CNE 135 fetches the measurement reports
and preprocess them to remove unwanted or corrupted entries. In
block 1640, the CNE 135 determines which of the plethora of
estimation approaches to use. In block 1645, the CNE 135 generates
RSRP predictions using a prediction tool (if applicable), and uses
the chosen approaches to estimate the error-prone BS configuration
parameters or their likelihood scores, and post-process them. In
block 1650, based on the results of the estimation, the CNE 135 may
take corrective actions such as correcting the parameter database,
dispatching a site engineer, initiating new network reconfiguration
application, and the like.
[0135] The above flowcharts illustrate example methods that can be
implemented in accordance with the principles of the present
disclosure and various changes could be made to the methods
illustrated in the flowcharts herein. For example, while shown as a
series of steps, various steps in each figure could overlap, occur
in parallel, occur in a different order, or occur multiple times.
In another example, steps may be omitted or replaced by other
steps.
[0136] Although the present disclosure has been described with an
exemplary embodiment, various changes and modifications may be
suggested to one skilled in the art. It is intended that the
present disclosure encompass such changes and modifications as fall
within the scope of the appended claims. None of the description in
this application should be read as implying that any particular
element, step, or function is an essential element that must be
included in the claims scope.
* * * * *